Harnessing the Web to Streamline Statistical Programming Processes

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1 Paper TT03 Harnessing the Web to Streamline Statistical Programming Processes Daniel Boisvert, Biogen, Cambridge MA, USA Pankaj Kumar, Biogen, Cambridge MA, USA Gordon Schantz, Biogen, Cambridge MA, USA ABSTRACT Custom web applications allow Biogen to produce highly extensible, data-driven niche systems with low overhead and relatively short development cycles. Over time, Biogen has identified specific areas that can be automated or simplified and has created a set of interconnected applications that provide a controlled and efficient environment for statistical programming. The three core applications are: Workflow - a request tracker, Phoenix - an analysis dataset designer and define.xml authoring tool, and Venus - an electronic table shell maker. This paper will introduce our application suite, our development approach and our future vision, to examine the possibilities of custom web development for statistical programming. CHALLENGES IN THE STATISTICAL PROGRAMMING PROCESS A statistical programming process involves all the tasks listed above as well as the project management of each. Typically, statistical programmers do not have access to interconnected off the shelf systems to aid them in their work. Instead they may have home-grown SAS macros mixed with Excel trackers. These have obvious limitations. The process begins with the creation of mock table shells, which serve as the specifications for the analyses and as an addendum to the statistical analysis plan. Statisticians are responsible for this document and tend to draft their mock shells in MS-Word. This is a time-consuming task that does not make any of the information available for downstream usage. Often, they are frustrated by spending time formatting columns, aligning mock numbers and constantly having to revisit the document to keep it in sync with the changing outputs. Also, table standards are difficult to enforce with this manual approach. Statisticians may initially reference the standard, but over time will reuse existing mock shell documents, not taking advantage of new versions as they are deployed. For SDTM and ADaM dataset creation, a basic approach involves writing specifications in Word. When the SAS program is drafted, metadata stored in the Word document will be manually reentered into the SAS program, and manually input to the define.xml (e.g., dataset label, dataset sort order, variable label, variable position). A more advanced solution would be to keep that metadata in Excel and use a SAS program to extract and apply it in the code. Excel, however, is limited in its functionality. For example, there is no concept of an audit trail in Excel. Secondly, individual Excel files do not force all information to be in a single central location. This reduces the ability to quickly locate specifications for any analysis. Lastly, variables that exist in one dataset have to have their metadata copied over to downstream datasets where the same variable exists. For example, ADSL.RACE would have to be redefined in ADLB. A time-consuming process ensues as the two variables would have to be kept in sync as their attributes change. Dataset and table, listing and figure creation (we refer to each as a request) require a high level of project management to be successful. Stakeholders constantly ask to be updated on the completeness of deliverables and need to know the QC state of each. As the size of the analysis project grows and the number of programmers working on it increases, understanding the total amount of work completed and total amount left to do quickly becomes a very difficult question to answer. In a typical process, a primary programmer would draft the program 1

2 then the QC programmer to tell them to start QC. The back and forth, until the output is finalized, would be tracked through . With this information locked in individual s, it becomes near impossible for anyone to know the QC state of all requests. Also, without a centralized database to monitor each request and who is working on it, programmers workload is hard to track. If one is overloaded and one is needing work, it is up to the lead programmer to make this distinction thereby distracting them from their programming tasks. It is due to these limitations and the lack of commercially available software that we decided to build bespoke web applications to aid statistical programming. APPLICATION DEVELOPMENT APPROACH When developing an application, it is important to note that a system does not correct a poor business process. Time must be taken to understand the tasks at hand and identify clear use cases where an application can provide value. We have found that it is better for the process to exist prior to trying to automate it. While sounding counterintuitive, we do not recommend creating a new business process for the sake of new technology. Instead, begin by creating a new business process leveraging standard technology (paper based, excel based, based) then use the lessons from that to provide requirements for the system. When running a low-tech process users may realize the process does not often occur, or works well without technology, or is completely impossible without technology. It is the last case in which we invest. WHY WEB? Accessibility: Users only need a web browser, which exists on virtually all modern computers. The full application suite is easily accessible via a URL without any separate software installation. Open source, no licensing: Our applications are written predominately in PHP, JavaScript, Python, and MySQL, which are available for use with no licensing fees and are backed by large user communities. Packages to automate common tasks are available and can be applied with little configuration (e.g.. PDF generation). Rapid development: Web applications are fast to develop and troubleshoot because they are predominately based on modules and procedures that have already undergone a QA process and do not need to be reinvented. Since changes do not need to be compiled to debug, the developers can unit test their changes immediately. Easy Deployment: Global updates can be rolled out easily because all the changes are applied on the server side and no reinstallation is required. APPLICATION DESCRIPTIONS WORKFLOW - STEAMLINING PROJECT MANAGEMENT Clinical trial analysis involves a lot of moving pieces. An analysis consists of many datasets and TLFs, being created by a team of programmers statisticians. The project management of this is itself a full-time job. Wanting analysts to focus on analytics instead of project management, we decided to build an easy-to-use system to track all the work of statistical programmers. It was out of this necessity that our first application was built, Workflow. Since its inception in 2005 Workflow has grown to be a hub of tools to optimize statistical programming work. Workflow, at its core, tracks requests through their work cycle. A request may start with a programmer, then go to a QC programmer, then back, then on to a statistician then finally to medical writing. Each of these transactions is tracked, and the data is recorded, by Workflow. Beyond this core functionality, Workflow has grown to automate other statistical programming processes. Among these are running a program or a batch of programs, scheduling a batch, creating a compiled PDF, tracking dependencies, and comparing subsequent runs to automate QC. A key asset of Workflow is the amount of data that it contains. Every work product (dataset specs, dataset code, TLF codes) produced over the last 12 years have their history recorded in Workflow. This includes every transaction between programmers, statisticians and medical writers related to analyses. This data is used in project management dashboards and management metric reviews. KEY FEATURES Project management Using Workflow as a project management tool allows programmers to monitor their study by seeing an overview of the study progression by status (development, QC or under Stat review). Workflow stores tracking history of every request in a centralized database, which, in turn, makes the data readily available for study/resources analysis and reporting 2

3 Interface to run SAS Being able to run SAS program through Workflow allows programmers to manage their projects along with creating and updating outputs without having them to switch to a different application. Adding a Web user interface to a SAS macro can make training and use more intuitive. For example, we have an interface to pull data from EDC which is accomplished with a SAS macro to which we pass the schema and dataset names. Then the SAS code executes as usual and creates the output files and log. To the user, it is just a couple clicks and then the data is placed in the correct location. A record of the transaction is saved in the Workflow database. Metrics Reporting / Resourcing The Workflow data enables us to generate various type of reports. The study quality report, workload spreadsheet and comparative effectiveness of vendors are some of the examples. SCREENSHOTS 3

4 VENUS STREAMLINING MOCK SHELL CREATION Venus is an electronic table shell designer. Users, typically statisticians, use Venus to implement standard table shells and to create study specific ones. To ensure compliance, Venus controls access and does not allow users to edit standard shells. Venus has solely the functionality required to make table shells. Users can enter text, indent text, insert columns, insert rows. This straightforward approach, chosen over a full-fledged rich text editor, allows the statisticians to focus on their exact needs. KEY FEATURES Paper to Data Table shells are historically kept in Word, making it difficult to mine or leverage the data later. Venus provides an interface for users to enter table shells and save that information as data. This centralized database enables Venus integration with other applications and opens the opportunity to automate table generation. Leverage Data in Other Applications Centralized Data Definition Venus automatically creates requests in Workflow and inserts titles and footnotes which are then received by the SAS program and added to the output. There is no chance of titles and footnotes getting out of sync from the shell to the output. Data can be written once and used multiple times. In Venus, treatment labels can be authored centrally then serve as the default when a new shell is created. This allows statisticians to update treatment headers in one place and have it propagate as requested to all the tables. SCREENSHOTS MAIN SCREEN 4

5 STANDARD TABLE IMPORT PHOENIX - STREAMLINING DATASET SPECIFICATIONS Phoenix was initially conceived to facilitate the creation of define.xml files from XPT dataset files, as a replacement to a notoriously buggy off-the-shelf product that the company had been using for this purpose. It stands as a good example of the user community steering design and development, because it has evolved from this simple task to be a dataset design tool. Historically, time-consuming and error prone manual processes were used to write analysis dataset specifications in Word then update the SAS program referencing the metadata in the document. If the document was updated, the SAS program would have to be updated manually, and there were no automated checks in place to ensure the two were kept in sync. At submission time, the define.xml would be created from the datasets and the Word specifications. Since we already had a tool that stored the study's metadata and could create the XML output, it was a short step to optimize the process, removing the need for Word, Excel, and external documents. KEY FEATURES Audit Trail Enforce Standards By implementing version history when records are saved, it is possible to determine who changed what and when. This is useful not only in the case of a system audit, but if a comment or origin suddenly contains an unexpected value and needs to be tracked back to the source of the edit. Phoenix includes a set of version reports and something resembling "diff" in the Unix world that can show all changes in a dataset that occurred between any two dates. Beyond versioning, we have also deployed an annotation system that saves its data outside of the study metadata, where programmers and statisticians can leave commentary and have conversations about a given variable. When creating a dataset, SDTM and/or ADaM standards are met through a simple process of automatically including all required variables and only allowing the addition of variables valid to the given domain. Phoenix then fills in any appropriate information 5

6 such as Role, Core or Label, avoiding the likelihood of typographical errors. Likewise, the correct codes for controlled terminology for multiple CDISC versions are stored and available for use, with all necessary codes, submission values, and preferred terms available. Links Define.xml generator Synchronization of data between domains is further ensured by variable links. If the base variable is edited, the edit propagates throughout the study in all ADaM datasets that are deriving this variable. So for example, if the length of DM.AGEU is modified, the change automatically carries over to ADSL.AGEU, ADEXP.AGEU, etc. with no extra effort on the part of the user. The original purpose of Phoenix remains one of the most compelling, because literally, clicking a single button on the screen results in the creation of a complete define.xml file which is then ready for validation against the data that will be submitted to the regulators. SCREENSHOTS DATASET MAIN PAGE CREATE A VARIABLE 6

7 EDIT VARIABLE SCREEN FUTURE As we start to think about the next steps for our application suite, we are exploring ways to take all the data that we have amassed and put it to work for us. There are two main projects under review, new analytics and more automation. ANALYTICS PREDICT REQUEST ASSIGNMENT Workflow contains information about all tables listings and figures over the last 12 years and everything that is due in the near future. When creating a new request, the user must think to whom to assign the tasks. The lead programmer might do a quick subjective assessment about the current workload or who is good at this type of request. Based on the data that Workflow can access, we can do a much more rigorous assessment and are able to predict who would be the best fit for the request. This will allow better use of resources and uncover programmers who are strong in a particular area (e.g., graphing specialists, analysis dataset specialists). For this project we are considering total turnaround time, number of iterations to get the output correct, number of iterations based on programming error, current number of requests assigned, and historical proficiency. PREDICT TOTAL WORK While we have robust prediction for core planned study work, it is harder to anticipate the exact number of datasets and outputs that will be requested over the life of the study. Typical resourcing algorithms will predict towards a deliverable (eg. CSR), but do not tell the full story of demand. Ask any programmer and they will tell you of how many requests come in around conferences, how much work is done on a study after it is submitted, or how work is still being done on that one study from 10 years ago. Now that we have so many years worth of actual data we feel this prediction is more possible. The end goal would be to be able to input a few key parameters (e.g., phase, indication, # subjects) and get the month by month total work demand for a yet to be started study. AUTOMATION At this point we have individual solutions to optimize specific pieces of functionality. Now we are starting to connect our systems. The first connection that we made was to connect Workflow and Phoenix. Workflow now contains a request for a dataset specification along with a request for the actual dataset. Depending on the status of the specification request, Phoenix will lock the specification from editing thus ensuring the specification and the dataset are kept in sync. Venus (our electronic table shell creator) and Workflow (our request tracker) are now also connected. The table shell outputs of Venus are the requests the programmers track in Workflow. Instead of maintaining the data in two places, Venus will become the interface to create and manage TLF requests. This will ensure the table specifications are always reflecting the actual output. Our long term goal for Venus is to extend it into a table creator. We plan to go beyond the shell and automate the table creation process using only the web application for printing and Python for statistical calculations. This will allow for fast, flexible and easy table creation. As soon as a shell has been specified it will have also been programmed and only awaits the incoming data to be complete. To date we have completed a successful proof of concept and will continue to develop this method. 7

8 CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the authors at: Daniel Boisvert Biogen biogen.com Pankaj Kumar Biogen biogen.com Gordon Schantz Biogen Biogen.com Brand and product names are trademarks of their respective companies. 8

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